Influencer Identification on Link Predicted Graphs

How would admissions look like in a it university program for influencers? In the realm of social network analysis, influence maximization and link prediction stand out as pivotal challenges. Influence maximization focuses on identifying a set of key nodes to maximize information dissemination, while link prediction aims to foresee potential connections within the network. These strategies, primarily deep learning link prediction methods and greedy algorithms, have been previously used in tandem to identify future influencers. However, given the complexity of these tasks, especially in large-scale networks, we propose an algorithm, The Social Sphere Model, which uniquely utilizes expected value in its future graph prediction and combines specifically path-based link prediction metrics and heuristic influence maximization strategies to effectively identify future vital nodes in weighted networks. Our approach is tested on two distinct contagion models, offering a promising solution with lower computational demands. This advancement not only enhances our understanding of network dynamics but also opens new avenues for efficient network management and influence strategy development.Comment: 19 pages + appendix. V2 has additional information on how our model differs from existing algorithm

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Social Network Opinion and Posts Mining for Community Preference Discovery

The popularity of posts, topics, and opinions on social media websites and the influence ability of users can be discovered by analyzing the responses of users (e.g., likes/dislikes, comments, ratings). Existing web opinion mining systems such as OpinionMiner is based on opinion text similarity scoring of users\u27 review texts and product ratings to generate database table of features, functions and opinions mined through classification to identify arriving opinions as positive or negative on user-service networks or interest networks (e.g., Amazon.com). These systems are not directly applicable to user-user networks or friendship networks (e.g., Facebook.com) since they do not consider multiple posts on multiple products, users\u27 relationships (such as influence), and diverse posts and comments. In this thesis, we propose a new influence network (IN) generation algorithm (Opinion Based IN:OBIN) through opinion mining of friendship networks (like Facebook.com). OBIN mines opinions using extended OpinionMiner that considers multiple posts and relationships (influences) between users. Approach used includes frequent pattern mining algorithm for determining community (positive or negative) preferences for a given product as input to standard influence maximization algorithms like CELF for target marketing. Experiments and evaluations show the effectiveness of OBIN over CELF in large-scale friendship networks. KEYWORDS Influence Analysis, Recommendation, Ranking, Sentiment Classification, Large Scale Network, Social Network, Opinion Mining, Text Mining

Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.Comment: Extended version of the conference paper at ASONAM 2017, 11 page